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1. Introduction

The use of antibiotics is continually being challenged by the emergence of resistant
strains of bacteria [1]. Resistance has resulted both
from mutations associated with bacterial DNA replication and from horizontal
gene transfer [2, 3]. However, while antibiotic
resistance is increasing, progress in the discovery of new antimicrobial
compounds is not [4]. The current trends in drug
resistance and drug discovery could result in future crises in treating microbial infections.

Currently, antibiotic therapy is based on achieving and exceeding a minimum inhibitory
concentration (MIC) for a sufficient amount of time in infected tissues [5]. The ability to
surpass and exceed greater than MIC in target tissues is influenced by the susceptibility of the pathogen to a
given antibiotic and the ability of the antibiotic to partition into the target
tissue. However, when faced with an infection caused by an antibiotic-resistant
bacterial strain, it may not be possible to surpass the MIC in the target tissue.
In this situation, one therapeutic option is treatment with a sub-MIC concentration
of the antibiotic. Sub-MIC antibiotic therapies can, however, lead to treatment
failure and antibiotic resistance [2]. Therefore, careful
evaluation of sub-MIC effects on bacterial physiology is needed prior to therapeutic
use of sub-MICs.

Pasteurella multocida is an important Gram-negative
zoonotic respiratory bacterial pathogen with a broad host range [6–10], and it is a particularly
good model organism to study antibiotic effects because it has a Gram-negative
envelope that is permeable to hydrophobic molecules (including antibiotics) [11, 12]. Therefore, investigating antibiotic effects
is possible with fewer confounding effects caused by permeability differences
between antibiotics.

Sub-MICs of antibiotics model the conditions that bacteria face in wild environments and
have evolved to survive [13]. Sub-MICs of antibiotics cause
defined effects on, and responses in, bacterial physiology. For example, although
sub-MICs of chlortetracycline reduced virulence factor expression in the bovine
respiratory pathogen Mannheimia haemolytica [14] they caused secondary or
“nontarget” effects, in addition to their primary target effects, in Pasteurella multocida [15]. Some of these secondary
effects may enhance the primary activity of the antibiotic, while others, such
as an increased expression of recombinase A (RecA) in response to enrofloxacin,
are apparent compensatory mechanisms.

Molecular systems level analysis including interaction networks can identify not only direct and
indirect global responses of bacterial genes to sub-MICs, but may also identify
key elements in genetic networks (regulatory hubs) that, when altered, change
the fundamental properties of these networks [16]. Identification of key
targets in the context of sub-MIC antibiotic effects on virulence factors
necessary for pathogenesis, and leading to antimicrobial resistance, can help
assess the benefit versus risk of sub-MIC therapeutic usage. Systems approaches
can also identify key molecules as potential targets for novel therapeutic combinations.

Here, we report a systems analysis of the Pasteurella multocida Pm70 response to sub-MICs of three
different antibiotics that differ in their mode of action: amoxicillin (AMX), which is a cell wall
biosynthesis inhibitor; chlortetracycline (CTC), which inhibits protein
synthesis; enrofloxacin (ENR), which is a quinolone that inhibits DNA
gyrase and DNA topoisomerase IV [17]. We used three different
classes of antibiotics to find common themes to antibiotic effects and the bacterial
adaptive response. Of these three antibiotics, resistant strains of P. multocida are described for
penicillins and tetracyclines [18]. Currently, no
fluoroquinolone resistant P. multocida are described; however, resistance to this antibiotic is documented for other Gram-negative
pathogens [19]. Using a liquid
chromatography tandem mass spectrometric proteomics approach, we identified 53%
of the predicted P. multocida proteome
expressed under our experimental conditions. The protein expression data was
analyzed in the context of interaction networks using Pathway Studio (Ariadne, Rockville, Md,
USA), which has canonical bacterial interaction networks. Overall compensatory response of P. multocida to sub-MICs was
characterized by altered expression of virulence factors; proteins involved in
purine metabolism, stress response, and cell envelope permeability.

2. Materials and Methods

2.1. Pasteurella Multocida Culture

Pasteurella multocida strain Pm70, a serotype A : 1 poultry isolate that has a fully
sequenced genome [20], was used in this study. Pm70
was cultivated in brain heart infusion (BHI) broth at 37°C with rotary
aeration. Minimum inhibitory concentrations (MICs) of AMX, CTC, and ENR for
Pm70 are 0.5 μg/mL, 4 μg/mL, and 0.031 μg/mL, respectively [15]. Growth
kinetics of Pm70 in the presence of 1/4 MIC of the three antibiotics were previously
described [15]. Stationary phase cultures of
Pm70 were used to inoculate 50 mL BHI to an initial A600 of 0.05;
antibiotic treated cultures contained 1/4 MIC of AMX, CTC, or ENR, and control cultures
were grown without antibiotics. All cultures were grown in triplicate to
mid-log phase (A600 of 0.8) and harvested by centrifugation (10 000 × g, 10 minutes, 4°C). Pellets were
stored at −80°C.

Two-dimensional liquid chromatography (LC) analysis was done by strong cation exchange (SCX)
followed by reverse phase (RP) coupled directly in line with an electrospray
ionization (ESI) ion trap tandem mass spectrometer (LCQ; ThermoElectron Corp.,
San Jose, Calif, USA) essentially as described in [15]. The salt gradient applied in
this study was different from the published method and was applied in steps of
0, 10, 15, 20, 25, 30, 35, 40, 45, 50, 57, 64, 90, and 700 mM ammonium acetate
in 5% acetonitrile (ACN) and 0.1% formic acid. The reverse phase gradient used
0.1% formic acid in ACN and increased the ACN concentration in a linear
gradient from 5% to 30% in 20 minutes and then 30% to 95% in 7 minutes,
followed by 5% for 10 minutes for 0, 10, 15, 25, 30, 45, 64, 90, and 700 mM
salt gradient steps. For 20, 35, 40, 50,
and 57 mM salt gradient steps, ACN concentration was increased in a linear
gradient from 5% to 40% in 65 minutes, 95% for 15 minutes, and 5% for 20 minutes.

All database searches of tandem mass spectra were done using TurboSEQUEST (Bioworks Browser
3.2; ThermoElectron) [21]. Mass spectra and tandem mass
spectra were searched against an in silico trypsin-digested protein database of P. multocida Pm70
downloaded from National Center for Biotechnology
Institute (NCBI). Cysteine carbamidomethylation and methionine oxidation
(single and double) were included in the search criteria. We used the reverse
database functionality in Bioworks 3.2 and searched tandem MS (MS2)
data against a reversed Pm70 database using the same search criteria as
described above. Peptide identifications
from Pm70 protein database that were
>5 amino acids long with Xcorr ≥ 1.5, 2.0, and 2.5 for +1, +2, and +3
charged ions, respectively, and with delta Cn values of ≥0.1 were used for
protein identifications [15]. None of the proteins
identified at the applied Xcorr and delta Cn filters had peptides identified
from the reversed Pm70 database. Protein identifications have been submitted to
PRoteomics IDEntifications (PRIDE) database [22] and the accession numbers are
1751, 1752, 1753, and 1754. PRIDE submission requirements are based on the
proposed guidelines by proteomics standards initiative [23] and include all the peptides identified for each protein with their
sequence, charge state, Xcorr, and delta cn.

We used an isotope-free
quantification method [14] and a custom program ProtQuant [24] to identify differences in
protein expression between control and sub-MIC antibiotic treated P. multocida. ProtQuant is a java-based
tool for label-free quantification that uses a spectral counting method [25] with increased specificity. ProtQuant includes the quantitative
aspects of the Sequest cross correlation (XCorr) into the spectral counting
method and computes the statistical significance of differential protein expression
using one-way ANOVA (α ≤ 0.05). This method requires
at least 3 peptides identified from either the control or treatment datasets.

To study the effects of antibiotics on Pm70 using
systems biology approaches, we built an information rich predicted protein
interaction network using Pathway Studio (Ariadne, Rockville, Md, USA) using a bacterial
molecular interaction database. Multiple aspects of protein function, including
protein modifications, cellular location, protein-protein interactions, gene
expression regulation, molecular transport and synthesis, and regulation of
various cellular processes are included [26].

Although the bacterial molecular interaction database
contains data for Gram-negative and Gram-positive bacteria, it does not include P. multocida. Therefore, to
append the bacteria database with P. multocida, all 2015 Pm70
proteins were mapped to their corresponding orthologs in the database by identifying
reciprocal-best-BLAST hits with greater than 30% similarity. Gram-positive orthologs were removed to
ensure that protein interaction networks were derived only from Gram-negative species.
The resulting ortholog map file was imported into Pathway Studio to allow prediction
of interactions between P. multocida proteins. The Gram-negative ortholog-only network was used to analyze
sub-MIC antibiotic effects on the P. multocida proteome. Our protein quantification method did not measure
absolute fold changes in protein expression, but instead indicated whether the increase or
decease in expression was significant. Therefore, for each antibiotic
treatment, we imported all identified proteins into Pathway Studio along with
the expression values indicating the significance of expression change; we
represented a significant increase in protein expression compared to the
untreated control as +1, while a significant decrease in expression was
represented as −1.

As an initial screening method, we used the Pathway Studio
“Find groups” tool to identify Gene Ontology (GO) groups that had a significant
number of identified proteins within each paired antibiotic treatment/control
protein set. By doing so we identified GO groups that had good proteome
coverage for analysis of antibiotic effects. Pathway Studio calculates the
statistical significance of the overlap between the protein list and a GO group
using the Fisher exact test. Thus, the calculated p-value depends on the extent
of overlap between the protein list and a group as well as the sizes of the list
and a group. We used P ≤ .05 to select GO groups with significant
protein coverage.

We built interaction networks in Pathway Studio with
proteins of interest including the upstream regulators and downstream targets.
In the interaction networks, different colors were used for the nodes to
indicate whether a protein was present in the dataset (pink) and whether there
was a significant increase (red), or decrease (green) in protein expression in
response to sub-MIC of antibiotic. Entities in the interaction map that were
not from Pm70 were shown in gray color. For each of the three antibiotics,
protein expression was compared between the control (no antibiotic) and the antibiotic
treatment and overlaid onto the Pm70 interaction network. The individual
responses to each antibiotic were compared to identify overall trends in Pm70 response
to the different antibiotics.

3. Results and Discussion

3.1. Proteome Coverage

We identified 1064 (53%) of the 2015 predicted Pm70 proteins [20] from our control (no
antibiotic) and antibiotic treated datasets. The number of identifications with
two or more peptides in at least one dataset was 572 (56%) (PRIDE datasets (supplementary Table 1)) and
this compares favorably with reported MudPIT results which have as few as 20% of
proteins identified by two or more peptides [27]. Furthermore,
compared to the 20% coverage of the accessible proteome that we reported with
ICAT [15], we achieved 2.5 fold higher proteome
coverage in this study. This level of proteome coverage gave us confidence
that we could conduct a systems-wide analysis to formulate new hypothesis for sub-MIC
antibiotic effects on Pm70 physiology.

Furthermore, because protein function depends on cellular location, a systems-wide analysis requires
that we identify proteins from all cell locations. We used the PSORTb version
2.0 bacterial protein subcellular localization prediction tool to classify all
proteins in our dataset [28]. PSORTb has an overall
precision of 96% and places all Pm70 proteins into seven categories: extracellular
(4), outer membrane (49), periplasmic (61), cytoplasmic membrane (411), cytoplasmic
(774), unknown (with multiple localization sites) (37), and unknown (679). To determine the percent coverage in each of
these categories, the number of proteins identified in each category was
compared to the total number of proteins predicted to be in that category from
the genome (Figure 1). The average coverage
for each subcellular compartment in our data was 59%, and although proteins
from all subcellular compartments were represented, not all subcellular
compartments were equally represented. We identified more outer membrane and
periplasmic proteins (84% and 75%, resp.), than cytoplasmic membrane proteins (36%).

Representation of P. multocida proteins in PSORTb subcellular locations. Percent coverage for each PSORTb predicted subcellular
localization for P.multocida proteins
identified in this study is shown. Percent representation was calculated by
comparing...

To investigate the lower coverage of cytoplasmic membranes, we estimated the total
abundance of cytoplasmic membrane and outer membrane proteins based on ΣXcorr [14]. The outer membrane proteins
had a ΣXcorr of 1599, while cytoplasmic membrane proteins had a ΣXcorr of 1424.
Therefore, although a lower percentage of cytoplasmic membrane proteins were
identified, there was a very similar amount of protein isolated from the cytoplasmic
and outer membrane fractions. This suggests that although the Pm70 cytoplasmic
membrane contains a greater variety of proteins than the outer membrane,
cytoplasmic membrane proteins are present in relatively low abundance compared
to the outer membrane. Because we used the mass spectrometer in a
data-dependent way to maximize proteome coverage, it is logical that we
achieved greater coverage of bacterial compartments that have proteins with
higher abundance. These results also show
that our methods for protein isolation solubilized relatively hydrophobic membrane
proteins. Our previously published ICAT methodology [15] and the current label-free
2D-LC ESI MS2 approach utilized the same protein isolation method. Nonisotopic
relative quantitative proteomics methods are as good (or better) than isotopic
methods [14, 29] but have the advantage of far
greater proteome coverage. We confirm this observation in the current work; compared
to our previous ICAT study we identified tenfold more membrane proteins, which
is 38.6% of the predicted membrane proteome of P. multocida compared with 3.7% identified using the ICAT method.

3.2. Pasteurella Multocida Pm70 Molecular Interaction Database

Predicted protein
interaction networks for P. multocida (based on computational comparative genomics) are
available [30]. However, these interactions
have edges that have no directionality or any biological information. System
analysis can be a powerful tool to identify global trends so long as the
protein interaction network under investigation is information rich. Therefore,
to enable system analysis of our protein expression data, we chose to build a
protein interaction network for P. multocida where all nodes had all possible biological information
associated with them. Reciprocal-BLASTp searches of Pm70 proteins against both Gram-negative
and Gram-positive bacterial proteins identified unique orthologs for only 1595 proteins.
Of these, 1525 were from Gram-negative bacteria (Escherichia coli CFT 073, E. coli K12, E. coli O157:H7, Synechocystis sp. PCC6803 (mean e-value
and percent identity were 2.03e-06 and 65.2%) of which 848 orthologs were identified
in our datasets. We constructed a network with 848 proteins in which the nodes
(proteins) had predicted functional annotation and the edges (links) between
proteins described regulation, expression, binding, and chemical reactions.

3.3. Systems Modeling: Data Analysis

To determine the effects of 1/4 MIC of each of the three
antibiotics, AMX,
CTC, and ENR, on the Pm70 proteome, protein expression from each of the three
antibiotic treatments was compared to protein expression from the nonantibiotic
control. We did not compare protein expression changes between antibiotic
treatments. The paired control antibiotic-treated samples contained 913,
781, and 762 proteins, representing 143, 133, and 137 GO groups, from AMX-, CTC-,
and ENR-treated cultures, respectively. Each GO group is a unique GO term that
corresponds to a biological process, molecular function, or a cellular
component annotation.

At P ≤ .05, 69, 61, and 58 GO groups for AMX,
CTC, and ENR, respectively had significant proteome coverage. Of these, 46 GO
groups were common to all three antibiotics (Table 1). To identify the
underlying higher level themes involved in the response to antibiotics, we used
the GoSlimViewer at AgBase [31] and found that the 46 common
GO groups had 16 higher level GO terms (Table 2).

GO slims common to sub-MIC AMX, CTC, and ENR response of P. multocida.

We identified 147, 126, and 134 significant changes
in protein expression with sub-MIC AMX, CTC, and ENR, respectively (Table 3,
and supplementary Table 2, all proteins were identified with at least three peptides in one dataset). A
number of proteins differentially expressed in response to sub-MIC antibiotics in
our previous ICAT study were also identified in this study. Although the
general trend of expression was similar (i.e., increase or decrease) compared
to ICAT, many of the differences were not statistically significant in this
study. This could be due to the data dependent acquisition inherent to ESI MS2 methodology combined with the
differences in both the amount of the proteome accessible
by ICAT and label-free proteomics and the difference in total coverage (numbers
of peptides/protein). We suggest that this nonisotopic proteomics workflow and
analysis offer more comprehensive coverage and a greater probability of
accurate representation of the Pm70 proteome than did our
ICAT study.

Differential P. multocida protein expression in response to a
quarter-MIC AMX, CTC, and ENR in different GO categories.

To identify common themes in the response to sub-MICs
of antibiotics, we superimposed significant changes in protein expression for
each antibiotic onto the Pm70 protein interaction network (supplementary Figure ). For more detailed analysis of selected
proteins, we used the Pm70 protein interaction network to iteratively build and
visualize networks around the proteins of interest. The network built around
RecA in response to ENR is shown in Figure 3 as an example of the type of
analysis that was used to identify the trends described in the following
paragraphs.

3.4. Common Adaptive Responses to AMX, CTC, and ENR

We examined the overall trends of antibiotic effects on the 16 GO groups that
were identified by GO SlimViewer (Table 2). After excluding general
GO terms like cytoplasm, metabolism and so forth, 13 GO
groups were evaluated. For each GO group, we calculated the percentage of
proteins whose expression either increased or decreased, compared to the total entities
in that GO group, for each antibiotic treatment. The trend of AMX was an
overall increase in protein expression, which could indicate the induction of
an adaptive response (Figure 2). Conversely, CTC and ENR had an overall
suppressive effect on protein expression, potentially indicating that CTC and ENR detrimentally
affect P. multocida fitness at doses below MIC. Alternatively, this could be a compensatory
response by slowing metabolism.

Overall trends in protein expression in GO Slims common
to sub-MICs. For each GO Slim, the percentage of proteins
whose expression was either up (black bars) or down regulated (gray bars)
is compared to the total entities in that GO Slim are shown for...

The overall suppressive effects of CTC and ENR on P. multocida metabolism are further substantiated by
their effects on individual protein expression in central metabolic pathways. In
glycolysis/gluconeogenesis, CTC and ENR significantly decreased the expression
of phosphoenolpyruvate carboxykinase, phosphoglycerate kinase,
fructose bisphosphate aldolase, and glyceraldehyde-3-phosphate dehydrogenase.
By contrast, AMX significantly increased the expression of fructose
bisphosphate aldolase, pyruvate kinase, and phosphoglyceromutase, and it
significantly increased expression of fumarate hydratase and succinyl-CoA
synthetase from the TCA cycle (Table 3).

The differential protein expression profiles of P. multocida in response to antibiotic sub-MICs were
analyzed to delineate the underlying physiological response. A predictable stress response was indicated
by increased GroES expression after treatment with all three antibiotics and increased
GroEL and DnaK expression after AMX treatment. The GroEL/GroES chaperone system
is induced by different forms of environmental stress in various bacterial
species and functions to maintain appropriate protein folding [32]. All antibiotics reduced the
expression of FtsH, a zinc metalloprotease that degrades cellular proteins,
including heat shock promoter protein sigma 32 (RpoH) [33]. Therefore, sub-MICs of
antibiotics may reduce expression of P. multocida genes under the control of heat shock promoters.

Sub-MICs of antibiotics induce a response that appears to decrease cell envelope permeability by increasing
the availability of N-acetyl glucosamine, a necessary precursor for peptidoglycan and lipid A biosynthesis.
E. coli mutants that are defective in lipid A biosynthesis are permeable to
hydrophobic molecules and highly susceptible to antibiotics [34, 35]; therefore, increasing the
amount of lipid A would be a logical response to decrease permeability. All
three antibiotics increased GlmS expression, which catalyzes the first reaction
in the pathway for N-acetylglucosamine biosynthesis, and AMX increased GlmU
expression, which catalyzes the last two reactions in the pathway. NagC, is a
transcriptional regulator controlling glmUS operon expression [36], its expression decreased
after CTC and ENR treatment. In addition to increasing N-acetylglucosamine
availability, CTC and ENR decreased expression of OmpA, a multifunctional outer
membrane porin that allows diffusion of small solutes across the outer membrane
[37, 38] (Table 3).

Purine metabolism was another common cellular function affected by all three
antibiotics. AMX, CTC, and ENR increased adenylate kinase expression. CTC and
ENR increased adenylosuccinate synthetase expression. Adenylate kinase and adenylosuccinate synthetase catalyze the first and last
steps in the conversion of inosine monophosphate to adenosine diphosphate [39], respectively. AMX and CTC
also significantly increased expression of PrsA, which catalyzes formation of phosphoribosyl
pyrophosphate, a necessary metabolite for synthesis of purines, pyrimidines,
and histidine.

RNA metabolism was affected by antibiotics; all increased RNA polymerase
expression; AMX increased RpoA, RpoB, RpoC, and RpoZ expression; CTC increased
RpoA expression; and ENR increased RpoZ expression. In addition, all three
antibiotics decreased expression of transcriptional terminator Rho, which may cause polarity suppression.
Decreased expression of Rho may also decrease the half life of mRNA in the bacterial cell [40].

It is possible that these protein expression trends in P. multocida that were common for all three
antibiotics could reflect a general response to stress. These changes could be induced by other
nonantibiotic stressors; further experiments with nonantibiotic stress
conditions would be required to confirm that these trends are in fact a common
response to antibiotic-induced stress.

3.5. Virulence Factor Expression

Sub-MICs of antibiotics can potentially impact the
outcome of infection by altering bacterial virulence factor expression [14, 41–43]. In the current study,
antibiotics altered expression of known and putative virulence proteins in Pasteurella multocida. For example, CTC
and ENR reduced the expression of OmpA, which in addition to allowing small
molecule diffusion across the outer membrane, is also a known virulence factor of P. multocida and is
involved in binding to host cells [44]. In addition, sub-MICs of CTC
and ENR decreased expression of detoxifying enzymes superoxide dismutase and
catalase, which constitute a major defense mechanism of bacteria against
reactive oxygen species and play a role in pathogenesis in certain bacteria [45, 46]. Other putative P. multocida virulence factors whose
expression was decreased by CTC and ENR included glyceraldehyde-3-phosphate
dehydrogenase, which is a virulence factor in Gram-positive bacteria and has
been recently implicated in pathogenesis of Gram-negative bacteria [47], and phosphoenolpyruvate
carboxykinase, which is required for M. bovis virulence in mice [48]. Stress response chaperone protein Hsp90 was
decreased by CTC and ENR, and DnaK was decreased by ENR. These proteins are required for virulence in
a number of bacterial pathogens including S. enterica and L. pneumophila [49, 50].

3.6. Amoxicillin Specific Effects

The AMX mode of action involves binding and inactivation of
peptidoglycan cross-linking transpeptidases with subsequent inhibition of cell
wall biosynthesis. The transpeptidases, also known as penicillin binding
proteins (Pbps), are known targets of AMX [51] and the P. multocida genome
has eight Pbps [20]. Logically, the bacterial compensatory
response to AMX could involve increasing Pbp expression. Though we identified 5 Pbps in our dataset; there were no significant changes in expression of any of
these. Therefore, it appears that the adaptive response to AMX treatment does
not entail over expression of Pbps. Alternatively, more than 1/4 MIC of AMX may
be required to elicit over expression of Pbps in P. multocida.

There was good evidence that the P. multocida compensatory
response to AMX involves decreasing cell envelope permeability. In addition to
the previously described effects on GlmU and GlmS, expression of several
enzymes responsible for synthesis of lipopolysaccharide was significantly
increased. LpxB, an enzyme in the lipid A biosynthetic pathway, had
significantly increased expression. KdsA and RfaE, both of which are involved
in synthesis of core oligosaccharide [52], were significantly
increased. Expression of GalU, which is responsible for the synthesis of cell
envelope precursor UDP-galactose, was also increased.

The compensatory response to AMX also appeared to involve upregulation of cell division
proteins. Both FtsZ (essential for cell division) and FtsY had increased
expression. AMX also caused increased expression of DeoR: a DNA-binding
transcriptional regulator involved in the negative regulation of genes encoding
nucleotide catabolism enzymes.

3.7. Chlortetracycline Specific Effects

As could be predicted, the response to CTC involved the compensation against its inhibition of protein
translation. Elongation factor Tu was increased, as was methionyl-tRNA
synthetase (MetG), which initiates translation through tRNA(fMet) aminoacylation.
In addition, DeaD (CsdA), which participates in the assembly of the large
subunit of the ribosome [53], also had increased expression.

CTC also appeared to induce a protective response for the cell envelope. In addition to its
effects on GlmS, NagC, and OmpA, it caused an increase in expression of murein
lipoprotein Lpp, which is required for stabilization and integrity of the cell
envelope [54]. Lpp mutants are
hypersensitive to toxic compounds [55]. Finally, CTC caused
increased expression of Lrp, a transcriptional regulator that is a global
mediator of the leucine response, specifically branched amino acid transport [56].

3.8. Enrofloxacin Specific Effects

Results from the current study agree with our
earlier findings [15] that the
compensatory response to ENR entails recruitment of double stranded DNA damage
repair machinery to overcome the quinolone-mediated block on progression
of the replication fork. This response is logical because blockage of the
replication fork by quinolones is caused by accumulation of inactive
quinolone-bound intermediate with double stranded DNA breaks. We previously
reported an increase in expression of RecA, which catalyzes DNA strand exchange
during homologous recombination and double-stranded DNA break repair [15], in response to ENR. Here we
found that expression of RecA and RecN, another enzyme required for repair of
double stranded breaks in the chromosome, increased.

4. Conclusions

We achieved greatly improved coverage of the P. multocida proteome by detecting
unlabeled peptides using 2D LC with ESI MS2 compared to a similar
study we conducted using isotope coded affinity tag labeled (ICAT) peptides [15]. The advantage of ICAT
labeling is that we were able to report fold changes in protein expression;
however, the increased proteome coverage in the current study enabled us to
conduct systems analysis, which was not possible in the ICAT study.

Systems analysis improved our study of P. multocida protein expression and
enabled the identification of antibiotic-mediated mechanisms and pathways. In
particular, visualizing proteins as interacting networks enabled us to identify
proteins with high connectivity that could be important targets for modulation.
However, although proteome coverage was not a major limitation in conducting
systems analysis with our P. multocida protein expression data, the paucity of experimental evidence from this
nonmodel species did limit our analysis. The strategy we used to identify
orthologs and import experimental evidence based on E. coli and other model species greatly enhanced our analysis, but
extrapolation of protein functions across species must be interpreted in the
light of this electronic inference.

Despite this limitation, our systems analysis
identified several proteins to target in future studies. In particular, we speculate
that inactivating P. multocida proteins
in the heat shock response, cell envelope biosynthesis/integrity, purine
metabolism, or RNA metabolism may be viable strategies to enhance antimicrobial
activity. In addition, inactivation of specific cell division proteins, translation
proteins, and DNA repair enzymes may impair bacterial adaptation to antibiotics
in the penicillin, tetracycline, and quinolone classes, respectively. Inactivation
of specific transcriptional regulators, such as DeoR or Lrp, may also be an
effective method to impair the P. multocida adaptive response against AMX and CTC. These targets would not
have been predicted a priori based on the known mechanisms of action of these antibiotics; their identification
was facilitated by our global systems analysis.

In summary, our findings could lead to the
development of new antimicrobial potentiating drugs aimed at inactivating antibiotic
compensatory mechanisms to prevent occurrence and emergence of antimicrobial
resistance. In addition, our results indicate that systems analysis in nonmodel
bacterial species can be used with high-throughput proteomics data to identify
protein targets for further functional investigations.

Supplementary Material

All proteins identified in this study are listed in Supplementary Table 1. Proteins with significant quantitative changes in expression in response to sub-MICs of AMX, CTC, or ENR are listed in Supplementary Table 2. Protein interaction networks built with significant changes in expression with sub-MICs shown in Table 3 are included in the supplementary figure. This figure shows changes in protein expression in response to sub-MICs of AMX, CTC, and ERN. Red and green indicate significant increase and decrease in expression, respectively. Proteins whose expression did not change (pink) and proteins in the network not identified in our datasets (gray) are also included.

Acknowledgments

This project was partially supported by a grant from the National Science Foundation (EPS-0556308-06040293)
and a competitive grant from Mississippi State University Life Sciences and
Biotechnology Institute (LSBI). We acknowledge Dr. Vivek Kapur for providing P. multocida pm70 strain, Tibor Pechan,
and Anton Yuryev for technical help. Mass spectrometry analysis was carried out at LSBI, Mississippi State University. This is a Mississippi Agricultural and Forestry Experiment Station publication (J11226), part of the publication costs were provided by the Department of Basic Sciences (CVM/MSU).

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36. Plumbridge JA. Repression and induction of the nag regulon of Escherichia coli K-12: the roles of nagC and nagA in maintenance of the uninduced state. Molecular Microbiology. 1991;5(8):2053–2062.[PubMed]